The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Alternative isoform regulation in human tissue transcriptomes
Open accessAbstract
Through alternative processing of pre-messenger RNAs, individual mammalian genes often produce multiple mRNA and protein isoforms that may have related, distinct or even opposing functions. Here we report an in-depth analysis of 15 diverse human tissue and cell line transcriptomes on the basis of deep sequencing of complementary DNA fragments, yielding a digital inventory of gene and mRNA isoform expression. Analyses in which sequence reads are mapped to exon-exon junctions indicated that 92-94% of human genes undergo alternative splicing, 86% with a minor isoform frequency of 15% or more. Differences in isoform-specific read densities indicated that most alternative splicing and alternative cleavage and polyadenylation events vary between tissues, whereas variation between individuals was approximately twofold to threefold less common. Extreme or 'switch-like' regulation of splicing between tissues was associated with increased sequence conservation in regulatory regions and with generation of full-length open reading frames. Patterns of alternative splicing and alternative cleavage and polyadenylation were strongly correlated across tissues, suggesting coordinated regulation of these processes, and sequence conservation of a subset of known regulatory motifs in both alternative introns and 3' untranslated regions suggested common involvement of specific factors in tissue-level regulation of both splicing and polyadenylation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Direct sequencing of RNA libraries also provides the opportunity to explore alternative splicing, a key mechanism that contributes to transcriptome diversity (Pan et al., 2008;Wang et al., 2008).”
From An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex (2014) · cites this paper as Wang et al., 2008
The cited source says what the claim attributes to it.
In the citing paper
“RNA sequencing (RNA-seq) studies indicate that ~93% of human genes undergo alternative splicing and about 86% have a minor isoform frequency above 15% (ref. 11).”
From How many human proteoforms are there? (2018) · cites this paper as [11]
The cited source says what the claim attributes to it.
In the citing paper
“RNA sequencing (RNA-seq) studies indicate that ~93% of human genes undergo alternative splicing and about 86% have a minor isoform frequency above 15% (ref. 11).”
From How many human proteoforms are there? (2018) · cites this paper as [11]
In the citing paper
“RNA-seq experiments have discovered over 100,000 AS transcripts in the human genome (Pan et al., 2008; Wang et al., 2008)”
From Splice-Junction-Based Mapping of Alternative Isoforms in the Human Proteome (2019) · cites this paper as Wang et al., 2008
In the citing paper
“Transcripts from ∼95% of multi-exon human genes are alternatively spliced (Pan et al., 2008, Wang et al., 2008).”
From Impact of Alternative Splicing on the Human Proteome (2017) · cites this paper as Wang et al., 2008
In the citing paper
“Several recent findingspropose that Rbfox can also regulate alternative polyadenylation (Wang et al., 2008) and mRNA stability (Ray et al., 2013), based on analysis of Rbfox motif sites in 3′ UTRs that are correlated with transcript abundance in different tissues.”
From HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism (2014) · cites this paper as Wang et al., 2008
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Initial sequencing and analysis of the human genome
International Human Genome Sequencing Consortium, Whitehead Institute for Biomedical Research, C. F. G. R., Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., Devon, K., Dewar, K., et al. (2001) · Nature · doi:10.1038/35057062 · Cited by 24,216
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855
Self-citationMechanisms of Alternative Pre-Messenger RNA Splicing
Black, D. L. (2003) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.72.121801.161720 · Cited by 2,704
MAQC Consortium, Shi, L., Shi, L., Reid, L. H., Jones, W. D., Shippy, R., Warrington, J. A., Baker, S. C., Collins, P. J., de Longueville, F., et al. (2006) · Nature Biotechnology · doi:10.1038/nbt1239 · Cited by 2,126
Self-citationXie, X., Lu, J., Kulbokas, E. J., Golub, T. R., Mootha, V., Lindblad-Toh, K., Lander, E. S., & Kellis, M. (2005) · Nature · doi:10.1038/nature03441 · Cited by 1,872
Genome-Wide Survey of Human Alternative Pre-mRNA Splicing with Exon Junction Microarrays
Johnson, J. M., Castle, J. C., Garrett-Engele, P. W., Kan, Z., Loerch, P., Armour, C. D., Santos, R., Schadt, E. E., Stoughton, R. B., & Shoemaker, D. D. (2003) · Science · doi:10.1126/science.1090100 · Cited by 1,418
HITS-CLIP yields genome-wide insights into brain alternative RNA processing
Licatalosi, D. D., Mele, A., Fak, J. J., Ule, J., Kayikci, M., Chi, S. W., Clark, T. A., Schweitzer, A. C., Blume, J. E., Wang, X., et al. (2008) · Nature · doi:10.1038/nature07488 · Cited by 1,352
Understanding alternative splicing: towards a cellular code
Matlin, A. J., Clark, F., & Smith, C. W. J. (2005) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1645 · Cited by 1,299
Sultan, M., Schulz, M. H., Richard, H., Magen, A., Klingenhoff, A., Scherf, M., Seifert, M., Borodina, T., Soldatov, A., Parkhomchuk, D., et al. (2008) · Science · doi:10.1126/science.1160342 · Cited by 1,293
Research works printed but not linked: 2
- Schroth, G. P., Luo, S. & Khrebtukova, I. Transcriptome analysis using high-throughput DNA sequencing. Methods Mol. Biol. (in the press)
- Pan, Q., Shai, O., Lee, L. J., Frey, B. J. & Blencowe, B. J. Deep surveying of alternative splicing complexity in the human genome by next generation sequencing. Nature Genet. (in the press)
Cited by
Sorted by citations
Items 301–400 of 1,022
Evolutionary dynamics of coding and non-coding transcriptomes
Necşulea, A., & Kaessmann, H. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3802 · Cited by 234
Slieker, R. C., Bos, S. D., Goeman, J. J., Bovée, J. V. M. G., Talens, R. P., van der Breggen, R., Suchiman, H. E. D., Lameijer, E.-W., Putter, H., van den Akker, E. B., et al. (2013) · Epigenetics & Chromatin · doi:10.1186/1756-8935-6-26 · Cited by 234
Ryan, M., Cleland, J., Kim, R., Wong, W. C., & Weinstein, J. N. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts452 · Cited by 233
Quantitative visualization of alternative exon expression from RNA-seq data
Yarden Katz, A., Wang, E. T., Silterra, J., Schwartz, S., Wong, B., Thorvaldsdóttir, H., Robinson, J. T., Mesirov, J. P., Airoldi, E. M., & Burge, C. B. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv034 · Cited by 233
Self-citationTargeted Intron Retention and Excision for Rapid Gene Regulation in Response to Neuronal Activity
Mauger, O., Lemoine, F., & Scheiffele, P. (2016) · Neuron · doi:10.1016/j.neuron.2016.11.032 · Cited by 233
The study of eQTL variations by RNA-seq: from SNPs to phenotypes
Majewski, J., & Pastinen, T. (2011) · Trends in Genetics · doi:10.1016/j.tig.2010.10.006 · Cited by 232
APPRIS: annotation of principal and alternative splice isoforms
Rodriguez, J. M., Maietta, P., Ezkurdia, I., Pietrelli, A., Wesselink, J.-J., Lopez, G., Valencia, A., & Tress, M. L. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1058 · Cited by 232
Designing a transcriptome next‐generation sequencing project for a nonmodel plant species1
Strickler, S. R., Bombarely, A., & Mueller, L. A. (2012) · American Journal of Botany · doi:10.3732/ajb.1100292 · Cited by 232
DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics
Nowicka, M., & Robinson, M. D. (2016) · F1000Research · doi:10.12688/f1000research.8900.2 · Cited by 232
Molecular Mechanisms of Toxicity of Silver Nanoparticles in Zebrafish Embryos
van Aerle, R., Lange, A., Moorhouse, A. J., Paszkiewicz, K. H., Ball, K., Johnston, B. D., De-Bastos, E. S. R., Booth, T. J., Tyler, C. R., & Santos, E. M. (2013) · Environmental Science & Technology · doi:10.1021/es401758d · Cited by 231
Vijay, N., Poelstra, J. W., Künstner, A., & Wolf, J. B. W. (2012) · Molecular Ecology · doi:10.1111/mec.12014 · Cited by 230
A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain
Joglekar, A., Prjibelski, A., Mahfouz, A., Collier, P., Lin, S., Schlusche, A. K., Marrocco, J., Williams, S. R., Haase, B., Hayes, A., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20343-5 · Cited by 229
HP1 Is Involved in Regulating the Global Impact of DNA Methylation on Alternative Splicing
Yearim, A., Gelfman, S., Shayevitch, R., Melcer, S., Glaich, O., Mallm, J., Nissim‐Rafinia, M., Cohen, A.-H., Rippe, K., Meshorer, E., et al. (2015) · Cell Reports · doi:10.1016/j.celrep.2015.01.038 · Cited by 229
Evaluating Gene Expression in C57BL/6J and DBA/2J Mouse Striatum Using RNA-Seq and Microarrays
Bottomly, D., Walter, N. A. R., Hunter, J. E., Darakjian, P., Kawane, S., Buck, K. J., Searles, R. P., Mooney, M. A., McWeeney, S. K., & HITZEMANN, R. J. (2011) · PLoS ONE · doi:10.1371/journal.pone.0017820 · Cited by 229
Dual function of C/D box small nucleolar RNAs in rRNA modification and alternative pre-mRNA splicing
Falaleeva, M., Pagès, A., Matuszek, Ż., Hidmi, S., Agranat-Tamir, L., Korotkov, K. V., Nevo, Y., Eyras, E., Sperling, R., & Stamm, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1519292113 · Cited by 229
MBNL proteins and their target RNAs, interaction and splicing regulation
Konieczny, P., Stępniak-Konieczna, E., & Sobczak, K. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku767 · Cited by 228
Survey of the transcriptome of Aspergillus oryzae via massively parallel mRNA sequencing
Wang, B., Guo, G., Wang, C., Lin, Y., Wang, X., Zhao, M., Guo, Y., He, M., Zhang, Y., & Pan, L. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq256 · Cited by 227
High-Resolution Sequencing and Modeling Identifies Distinct Dynamic RNA Regulatory Strategies
Rabani, M., Raychowdhury, R., Jovanovic, M., Rooney, M., Stumpo, D. J., Pauli, A., Hacohen, N., Schier, A. F., Blackshear, P. J., Friedman, N., et al. (2014) · Cell · doi:10.1016/j.cell.2014.11.015 · Cited by 226
Sequencing technology does not eliminate biological variability
Hansen, K. D., Wu, Z., Irizarry, R. A., & Leek, J. T. (2011) · Nature Biotechnology · doi:10.1038/nbt.1910 · Cited by 226
Calarco, J. A., Superina, S., O'Hanlon, D., Gabut, M., Raj, B., Pan, Q., Skalska, U., Clarke, L., Gelinas, D., van der Kooy, D., et al. (2009) · Cell · doi:10.1016/j.cell.2009.06.012 · Cited by 225
RNA Splicing and Disease: Animal Models to Therapies
Montes, M., Sanford, B. L., Comiskey, D. F., & Chandler, D. S. (2018) · Trends in Genetics · doi:10.1016/j.tig.2018.10.002 · Cited by 225
The Cancer Spliceome: Reprograming of Alternative Splicing in Cancer
Marabti, E. E., & Younis, I. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00080 · Cited by 224
Tilgner, H., Jahanbani, F., Blauwkamp, T., Moshrefi, A., Jaeger, E., Chen, F., Harel, I., Bustamante, C. D., Rasmussen, M., & Snyder, M. P. (2015) · Nature Biotechnology · doi:10.1038/nbt.3242 · Cited by 223
A global view of protein expression in human cells, tissues, and organs
Pontén, F., Gry, M., Fagerberg, L., Lundberg, E., Asplund, A., Berglund, L., Oksvold, P., Björling, E., Hober, S., Kampf, C., et al. (2009) · Molecular Systems Biology · doi:10.1038/msb.2009.93 · Cited by 222
TGF-β drives epithelial-mesenchymal transition through δEF1-mediated downregulation of ESRP
Horiguchi, K., Sakamoto, K., Koinuma, D., Semba, K., Inoue, A., Inoue, S., Fujii, H., Yamaguchi, A., Miyazawa, K., Miyazono, K., et al. (2011) · Oncogene · doi:10.1038/onc.2011.493 · Cited by 222
The relationship between proteome size, structural disorder and organism complexity
Schád, É., Tompa, P., & Hegyi, H. (2011) · Genome biology · doi:10.1186/gb-2011-12-12-r120 · Cited by 221
RNA-binding protein RBM20 represses splicing to orchestrate cardiac pre-mRNA processing
Maatz, H., Jens, M., Liss, M., Schäfer, S., Heinig, M., Kirchner, M., Adami, E., Rintisch, C., Dauksaite, V., Radke, M. H., et al. (2014) · Journal of Clinical Investigation · doi:10.1172/jci74523 · Cited by 221
Ge, Y. C., & Porse, B. T. (2013) · BioEssays · doi:10.1002/bies.201300156 · Cited by 219
Kaviar: an accessible system for testing SNV novelty
Glusman, G., Caballero-Pérez, J., Mauldin, D. E., Hood, L., & Roach, J. C. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr540 · Cited by 217
Extracellular-Regulated Kinases: Signaling From Ras to ERK Substrates to Control Biological Outcomes
Eblen, S. T. (2018) · Advances in cancer research · doi:10.1016/bs.acr.2018.02.004 · Cited by 217
Martín, G., Márquez, Y., Mantica, F., Duque, P., & Irimia, M. (2021) · Genome biology · doi:10.1186/s13059-020-02258-y · Cited by 217
Deep sequencing of the zebrafish transcriptome response to mycobacterium infection
Hegedűs, Z., Zakrzewska, A., Ágoston, V. C., Ordas, A., Rácz, P., Mink, M., Spaink, H. P., & Meijer, A. H. (2009) · Molecular Immunology · doi:10.1016/j.molimm.2009.07.002 · Cited by 216
RBFOX1 regulates both splicing and transcriptional networks in human neuronal development
Fogel, B. L., Wexler, E., Wahnich, A., Friedrich, T., Vijayendran, C., Gao, F., Parikshak, N., Konopka, G., & Geschwind, D. H. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds240 · Cited by 216
Widespread splicing changes in human brain development and aging
Mazin, P., Xiong, J., Liu, X., Yan, Z., Zhang, X., Li, M., He, L., Somel, M., Yuan, Y., Phoebe Chen, Y., et al. (2013) · Molecular Systems Biology · doi:10.1038/msb.2012.67 · Cited by 216
Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data
Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 216
Lim, K., Zhou, H., Jeon, H. Y., Kach, J., Jing, E., Weyn‐Vanhentenryck, S. M., Downs, M., Corrionero, A., Oh, R. S., Scharner, J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17093-9 · Cited by 216
Ab initio construction of a eukaryotic transcriptome by massively parallel mRNA sequencing
Yassour, M., Kaplan, T., Fraser, H. B., Levin, J. Z., Pfiffner, J., Adiconis, X., Schroth, G. P., Luo, S. J., Khrebtukova, I., Gnirke, A., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0812841106 · Cited by 215
Self-citationThe Dimensions, Dynamics, and Relevance of the Mammalian Noncoding Transcriptome
Deveson, I. W., Hardwick, S. A., Mercer, T. R., & Mattick, J. S. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.04.004 · Cited by 215
Pal, S., Gupta, R., Kim, H., Wickramasinghe, P., Baubet, V., Showe, L. C., Dahmane, N., & Davuluri, R. V. (2011) · Genome Research · doi:10.1101/gr.120535.111 · Cited by 215
Ferreira, P. G., Jares, P., Rico, D., Gómez-López, G., Martínez-Trillos, A., Villamor, N., Ecker, S., González-Pérez, A., Knowles, D. G., Monlong, J., et al. (2014) · Genome Research · doi:10.1101/gr.152132.112 · Cited by 214
Chen, Y., Davidson, N., Wan, Y. K., Yao, F., Su, Y., Gamaarachchi, H., Sim, A., Patel, H., Low, H. M., Hendra, C., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02623-4 · Cited by 214
Ince-Dunn, G., Okano, H. J., Jensen, K. B., Park, W.-Y., Zhong, R., Ule, J., Mele, A., Fak, J. J., Yang, C., Zhang, C., et al. (2012) · Neuron · doi:10.1016/j.neuron.2012.07.009 · Cited by 212
Alternative polyadenylation: New insights from global analyses
Shi, Y. (2012) · RNA · doi:10.1261/rna.035899.112 · Cited by 212
The roles of RNA processing in translating genotype to phenotype
Manning, K. S., & Cooper, T. A. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2016.139 · Cited by 212
Zhou, H.-L., Luo, G., Wise, J. A., & Lou, H. J. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt875 · Cited by 211
Sterne-Weiler, T., Weatheritt, R. J., Best, A. J., Ha, K., & Blencowe, B. J. (2018) · Molecular Cell · doi:10.1016/j.molcel.2018.08.018 · Cited by 210
Alternative Splicing and Protein Diversity: Plants Versus Animals
Chaudhary, S., Khokhar, W., Jabre, I., Reddy, A. S. N., Byrne, L. J., Wilson, C. M., & Syed, N. H. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.00708 · Cited by 209
Regulation of alternative splicing by the core spliceosomal machineryPreprint
Saltzman, A. L., Pan, Q., & Blencowe, B. J. (2011) · Genes & Development · doi:10.1101/gad.2004811 · Cited by 209
RNA Processing and ExportPreprint
Hocine, S., Singer, R. H., & Grünwald, D. (2010) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a000752 · Cited by 209
Warzecha, C. C., Shen, S., Xing, Y., & Carstens, R. P. (2009) · RNA Biology · doi:10.4161/rna.6.5.9606 · Cited by 208
Bainbridge, M. N., Wang, M., Wu, Y., Newsham, I., Muzny, D. M., Jefferies, J. L., Albert, T. J., Burgess, D. L., & Gibbs, R. A. (2011) · Genome biology · doi:10.1186/gb-2011-12-7-r68 · Cited by 208
RNA sequencing shows no dosage compensation of the active X-chromosome
Xiong, Y., Chen, X., Chen, Z., Wang, X., Shi, S., Wang, X., Zhang, J., & He, X. (2010) · Nature Genetics · doi:10.1038/ng.711 · Cited by 208
A Comprehensive Map of mRNAs and Their Isoforms across All 14 Renal Tubule Segments of Mouse
Chen, L., Chou, C.-L., & Knepper, M. A. (2021) · Journal of the American Society of Nephrology · doi:10.1681/asn.2020101406 · Cited by 208
SOAPfuse: an algorithm for identifying fusion transcripts from paired-end RNA-Seq data
Jia, W., Qiu, K., He, M., Song, P., Zhou, Q., Zhou, F., Yu, Y., Zhu, D., Nickerson, M. L., Wan, S., et al. (2013) · Genome biology · doi:10.1186/gb-2013-14-2-r12 · Cited by 206
Alternative splicing in aging and longevity
Bhadra, M., Howell, P., Dutta, S., Heintz, C., & Mair, W. B. (2020) · Human Genetics · doi:10.1007/s00439-019-02094-6 · Cited by 205
Prediction and Quantification of Splice Events from RNA-Seq Data
Goldstein, L. D., Cao, Y., Pau, G., Lawrence, M. F., Wu, T. D., Seshagiri, S., & Gentleman, R. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156132 · Cited by 205
Song, Y., Botvinnik, O. B., Lovci, M. T., Kakaradov, B., Liu, P., Xu, J. L., & Yeo, G. W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.06.003 · Cited by 204
The hitchhiker’s guide to PGC-1α isoform structure and biological functions
Martínez-Redondo, V., Pettersson, A., & Ruas, J. L. (2015) · Diabetologia · doi:10.1007/s00125-015-3671-z · Cited by 204
SF2/ASF autoregulation involves multiple layers of post-transcriptional and translational control
Sun, S., Zhang, Z., Sinha, R., Karni, R., & Krainer, A. R. (2010) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.1750 · Cited by 204
Alternative Splicing in Neurogenesis and Brain Development
Su, C.-H., Dhananjaya, D., & Tarn, W. (2018) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2018.00012 · Cited by 204
Advances in plant genome sequencing
Hamilton, J. P., & Buell, C. R. (2012) · The Plant Journal · doi:10.1111/j.1365-313x.2012.04894.x · Cited by 203
CELF1 preferentially binds to exon-intron boundary and regulates alternative splicing in HeLa cells
Xia, H., Chen, D., Wu, Q., Wu, G., Zhou, Y., Zhang, Y., & Zhang, L. (2017) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2017.07.004 · Cited by 203
Self-citationWilliamson, L. M., Saponaro, M., Boeing, S., East, P., Mitter, R., Kantidakis, T., Kelly, G. P., Lobley, A., Walker, J., Spencer‐Dene, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.01.019 · Cited by 201
Alternative splicing in neurodegenerative disease and the promise of RNA therapies
Nikom, D., & Zheng, S. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00717-6 · Cited by 201
Intron retention in mRNA: No longer nonsense
Wong, J. J. L., Au, A. Y. M., Ritchie, W., & Rasko, J. E. J. (2015) · BioEssays · doi:10.1002/bies.201500117 · Cited by 200
Alternative splicing and the evolution of phenotypic novelty
Bush, S. J., Chen, L., Tovar-Corona, J. M., & Urrutia, A. O. (2016) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2015.0474 · Cited by 200
Evaluation and comparison of computational tools for RNA-seq isoform quantification
Zhang, C., Zhang, B., Lin, L., & Zhao, S. (2017) · BMC Genomics · doi:10.1186/s12864-017-4002-1 · Cited by 200
Shalgi, R., Hurt, J. A., Lindquist, S. L., & Burge, C. B. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.04.044 · Cited by 199
Self-citationA Splicing-Independent Function of SF2/ASF in MicroRNA Processing
Wu, H., Sun, S., Tu, K., Gao, Y., Xie, B., Krainer, A. R., & Zhu, J. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.02.021 · Cited by 199
Hoang, N. V., Furtado, A., Mason, P. J., Marquardt, A., Kasirajan, L., Thirugnanasambandam, P. P., Botha, F. C., & Henry, R. J. (2017) · BMC Genomics · doi:10.1186/s12864-017-3757-8 · Cited by 199
Distinct Patterns of Expression and Evolution of Intronless and Intron-Containing Mammalian Genes
Shabalina, S. A., Ogurtsov, A. Y., Spiridonov, A. N., Novichkov, P. S., Spiridonov, N. A., & Koonin, E. V. (2010) · Molecular Biology and Evolution · doi:10.1093/molbev/msq086 · Cited by 198
Prognostic alternative mRNA splicing signature in non-small cell lung cancer
Li, Y., Sun, N., Lu, Z., Sun, S., Huang, J., Chen, Z., & He, J. (2017) · Cancer Letters · doi:10.1016/j.canlet.2017.02.016 · Cited by 198
Androgen receptor (AR) aberrations in castration-resistant prostate cancer
Waltering, K. K., Urbanucci, A., & Visakorpi, T. (2012) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2011.12.019 · Cited by 197
Bagnaresi, P., Biselli, C., Orrù, L., Urso, S., Crispino, L., Abbruscato, P., Piffanelli, P., Lupotto, E., Cattivelli, L., & Valè, G. (2012) · PLoS ONE · doi:10.1371/journal.pone.0051609 · Cited by 197
Martínez, N. M., & Lynch, K. W. (2013) · Immunological Reviews · doi:10.1111/imr.12047 · Cited by 197
New connections between splicing and human disease
Padgett, R. A. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.01.001 · Cited by 194
Modeling non-uniformity in short-read rates in RNA-Seq data
Li, J., Jiang, H., & Wong, W. H. (2010) · Genome Biology · doi:10.1186/gb-2010-11-5-r50 · Cited by 193
Licatalosi, D. D., Yano, M., Fak, J. J., Mele, A., Grabinski, S. E., Zhang, C., & Darnell, R. B. (2012) · Genes & Development · doi:10.1101/gad.191338.112 · Cited by 193
Li, Y., Dai, C., Hu, C., Liu, Z., & Kang, C. (2016) · The Plant Journal · doi:10.1111/tpj.13462 · Cited by 193
Global detection of human variants and isoforms by deep proteome sequencing
Sinitcyn, P. G., Richards, A., Weatheritt, R. J., Brademan, D. R., Marx, H., Shishkova, E., Meyer, J. G., Hebert, A. S., Westphall, M. S., Blencowe, B. J., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01714-x · Cited by 193
A comparison between ribo-minus RNA-sequencing and polyA-selected RNA-sequencing
Cui, P., Lin, Q., Ding, F., Xin, C., Gong, W., Zhang, L., Geng, J., Zhang, B., Yu, X., Yang, J., et al. (2010) · Genomics · doi:10.1016/j.ygeno.2010.07.010 · Cited by 192
Self-citationThe relevance of the TGF-β Paradox to EMT-MET programs
Morrison, C. D., Parvani, J. G., & Schiemann, W. P. (2013) · Cancer Letters · doi:10.1016/j.canlet.2013.02.048 · Cited by 192
BCLAF1 and its splicing regulator SRSF10 regulate the tumorigenic potential of colon cancer cells
Zhou, X., Li, X., Cheng, Y., Wu, W., Xie, Z., Xi, Q., Han, J. S., Wu, G., Fang, J. S., & Feng, Y. (2014) · Nature Communications · doi:10.1038/ncomms5581 · Cited by 192
Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns
Sproul, D., Kitchen, R. R., Nestor, C. E., Dixon, J. M., Sims, A. H., Harrison, D. J., Ramsahoye, B. H., & Meehan, R. R. (2012) · Genome Biology · doi:10.1186/gb-2012-13-10-r84 · Cited by 191
Impact of Alternative Splicing on the Human Proteome
Liu, Y., Gonzàlez-Porta, M., Santos, S., Brazma, A., Marioni, J. C., Aebersold, R., Venkitaraman, A. R., & Wickramasinghe, V. O. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.07.025 · Cited by 190
Comprehensive analysis of RNA-seq data reveals the complexity of the transcriptome in Brassica rapa
Tong, C., Wang, X., Yu, J., Wu, J., Li, W., Huang, J., Dong, C., Hua, W., & Liu, S. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-689 · Cited by 190
Alternative splicing in prostate cancer
Paschalis, A., Sharp, A., Welti, J. C., Neeb, A., Raj, G. V., Luo, J., Plymate, S. R., & Bono, J. S. D. (2018) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-018-0085-0 · Cited by 190
Huang, H., Zhang, J., Harvey, S. E., Hu, X., & Cheng, C. (2017) · Genes & Development · doi:10.1101/gad.305862.117 · Cited by 190
Wu, J. Q., Habegger, L., Noisa, P., Szekely, A., Qiu, C., Hutchison, S., Raha, D., Egholm, M., Lin, H., Weissman, S., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914114107 · Cited by 189
Fox-1 family of RNA-binding proteins
Kuroyanagi, H. (2009) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-009-0120-5 · Cited by 187
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186
The importance of DNA methylation of exons on alternative splicing
Shayevitch, R., Askayo, D., Keydar, I., & Ast, G. (2018) · RNA · doi:10.1261/rna.064865.117 · Cited by 186
Cleavage and polyadenylation: Ending the message expands gene regulation
Neve, J., Patel, R. J., Wang, Z., Louey, A., & Furger, A. (2017) · RNA Biology · doi:10.1080/15476286.2017.1306171 · Cited by 185
QAPA: a new method for the systematic analysis of alternative polyadenylation from RNA-seq data
Ha, K., Blencowe, B. J., & Morris, Q. (2018) · Genome biology · doi:10.1186/s13059-018-1414-4 · Cited by 184
De Novo Origin of Human Protein-Coding Genes
Wu, D.-D., Irwin, D. M., & Zhang, Y.-P. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002379 · Cited by 183
Alternative splicing of intrinsically disordered regions and rewiring of protein interactions
Buljan, M., Chalancon, G., Dunker, A. K., Bateman, A. G., Balaji, S., Fuxreiter, M., & Babu, M. M. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.03.006 · Cited by 183
Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing
Horner, D. S., Pavesi, G., Castrignanò, T., Meo, P. D. D., Liuni, S., Sammeth, M., Picardi, E., & Pesole, G. (2009) · Briefings in Bioinformatics · doi:10.1093/bib/bbp046 · Cited by 183
Aberrant RNA Splicing in Cancer and Drug Resistance
Wang, B., & Lee, N. (2018) · Cancers · doi:10.3390/cancers10110458 · Cited by 183
DISC1 splice variants are upregulated in schizophrenia and associated with risk polymorphisms
Nakata, K., Lipska, B. K., Hyde, T. M., Ye, T., Newburn, E. N., Morita, Y., Vakkalanka, R., Barenboim, M., Sei, Y., Weinberger, D. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903413106 · Cited by 182
Tahira, A. C., Kubrusly, M. S., Faria, M. F., Dazzani, B., Fonseca, R. S., Maracaja‐Coutinho, V., Verjovski‐Almeida, S., Machado, M. C. C., & Reis, E. M. R. (2011) · Molecular Cancer · doi:10.1186/1476-4598-10-141 · Cited by 182
Metadata
- Authors
- Wang, E. T., Sandberg, R., Luo, S., Khrebtukova, I., Zhang, L., Mayr, C., Kingsmore, S. F., Schroth, G. P., & Burge, C. B.
- Affiliations
- Massachusetts Institute of Technology (US)
- Karolinska Institutet (SE)
- Illumina (United States) (US)
- Whitehead Institute for Biomedical Research (US)
- National Center for Genome Resources (US)
- Corresponding author
- Burge, Christopher B.
- ORCID
- Venue
- Nature
- Published
- 2008-11
- Volume
- 456
- Issue
- 7221
- Pages
- 470-476
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC2593745
- OpenAlex
- W2155804510
- Cited by
- 5,181
- References
- 41
- Citations by year
- 2026: 172 · 2025: 222 · 2024: 253 · 2023: 254 · 2022: 317 · 2021: 364 · 2020: 294 · 2019: 304 · 2018: 266 · 2017: 262 · 2016: 294 · 2015: 316 · 2014: 345 · 2013: 347 · 2012: 376
- FWCI
- 108.59
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Human Genome Research Institute (R01HG002439)
- National Cancer Institute (P30CA008748)
- National Institute of General Medical Sciences (R01GM085319)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- alternative splicing, mRNA isoforms, tissue-specific gene expression, exon-exon junctions, sequence conservation, 3' untranslated region, human transcriptome, human cell lines, splicing regulation
- MeSH terms
- RNA Splicing Factors, Base Sequence, Cell Line, Exons, Humans, Organ Specificity, Repressor Proteins, RNA, Messenger, Open Reading Frames, RNA-Binding Proteins, Alternative Splicing, Protein Isoforms, Gene Expression Profiling, Polyadenylation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
